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Category: robotics/AI – Page 2

Project Silica’s advances in glass storage technology

As a research initiative, Project Silica has demonstrated these advances through several proofs of concept, including storing Warner Bros.’ “Superman” movie on quartz glass (opens in new tab), partnering with Global Music Vault (opens in new tab) to preserve music under ice for 10,000 years (opens in new tab), and working with students on a “Golden Record 2.0” project (opens in new tab), a digitally curated archive of images, sounds, music, and spoken language, crowdsourced to represent and preserve humanity’s diversity for millennia.

The research phase is now complete, and we are continuing to consider learnings from Project Silica as we explore the ongoing need for sustainable, long-term preservation of digital information. We have added this paper to our published works so that others can build on them.

Project Silica has made scientific advances across multiple areas beyond laser direct writing (LDW) in glass, including archival storage systems design, archival workload analysis, datacenter robotics, erasure coding, free-space optical components, and machine learning-based methods for symbol decoding in storage systems. Many of these innovations were described in our ACM Transactions on Storage publication (opens in new tab) in 2025.

Machine learning algorithm fully reconstructs LHC particle collisions

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly and precisely than traditional methods, helping physicists better understand LHC data. The paper has been submitted to the European Physical Journal C and is currently available on the arXiv preprint server.

Each proton–proton collision at the LHC sprays out a complex pattern of particles that must be carefully reconstructed to allow physicists to study what really happened. For more than a decade, CMS has used a particle-flow (PF) algorithm, which combines information from the experiment’s different detectors, to identify each particle produced in a collision. Although this method works remarkably well, it relies on a long chain of hand-crafted rules designed by physicists.

The new CMS machine-learning-based particle-flow (MLPF) algorithm approaches the task fundamentally differently, replacing much of the rigid hand-crafted logic with a single model trained directly on simulated collisions. Instead of being told how to reconstruct particles, the algorithm learns how particles look in the detectors, like how humans learn to recognize faces without memorizing explicit rules.

Tower Semiconductor and Scintil Photonics Announce Availability of World’s First Heterogeneously Integrated DWDM Lasers for AI Infrastructure

Combined with Tower’s multi-site global footprint, Scintil’s unique SHIP™ platform is ready to take on the challenging requirements of the next generation Hyperscale AI Infrastructure Scintil Photonics LEAF Light™ Scintil Photonics’ LEAF Light™ is the industry’s first single-chip DWDM-native light engine, delivering high-density, low-power optical connectivity for next-generation AI factories. MIGDAL HAEMEK, Israel and GRENOBLE, France, Feb. 17, 2026 (GLOBE NEWSWIRE) — Tower Semiconductor (NASD

Tomorrowland: You are a sophisticated analyst specializing in the implications of Al for the economy and markets

I am asking you for a report of no more than 3,000 words with deep analysis of which global sectors are likely to be most and least disrupted by Artificial Intelligence.

The following report and images are the Gemini output from the prompt I entered…

Sectoral Disruption and Economic Resilience 2026 I read the Deutsche Bank report, then ran the prompt against the latest version of Google Gemini 3 Pro. I didn’t have all their criteria, so I entered the basic prompt they had utilized.

The Truth About Merging With AI

Will humans one day merge with artificial intelligence? Futurist Ray Kurzweil predicts a coming “singularity” where humans upload their minds into digital systems, expanding intelligence and potentially achieving immortality. But critics argue that consciousness, creativity, love, and spiritual awareness cannot be reduced to algorithms. This discussion explores brain-computer interfaces, quantum mechanics and the mind, the Ship of Theseus identity paradox, and whether a digital copy of your brain would actually be you. Is AI-driven immortality possible—or does it misunderstand what it means to be human?

Every year the Center sponsors COSM an exclusive national summit on the converging technologies remaking the world as we know it. Visit COSM.TECH (https://cosm.tech/) for information on COSM 2025, November 19–21 at the beautiful Hilton Scottsdale Resort and Spas in Scottsdale, AZ. For more information. Registration will launch mid-July.

The mission of the Walter Bradley Center for Natural and Artificial Intelligence at Discovery Institute is to explore the benefits as well as the challenges raised by artificial intelligence (AI) in light of the enduring truth of human exceptionalism. People know at a fundamental level that they are not machines. But faulty thinking can cause people to assent to views that in their heart of hearts they know to be untrue. The Bradley Center seeks to help individuals—and our society at large—to realize that we are not machines while at the same time helping to put machines (especially computers and AI) in proper perspective.

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Can AI build a machine that draws a heart? What automated mechanism design could mean for mechanical engineering

Can you design a mechanism that will trace out the shape of a heart? How about the shape of a moon, or a star? Mechanism design—the art of assembling linkages and joints to create machines with prescribed motion—is one of the quintessential activities of mechanical engineers, but has resisted automation for almost two centuries.

In his seminal 1841 book Principles of Mechanisms, Oxford professor Robert Willis famously noted, “When the mind of a mechanician is occupied with the contrivance of a machine, he must wait until, in the midst of his meditations, some happy combination presents itself to his mind which may answer his purpose.”

Almost 200 years later, we still teach machine design mostly by apprenticeship. While we can simulate machines of almost any complexity, systematic methods for design are known only for the most trivial contraptions.

Machine learning helps solve a central problem of quantum chemistry

Within the STRUCTURES Cluster of Excellence, two research teams at the Interdisciplinary Center for Scientific Computing (IWR) have refined a computing process, long held to be unreliable, such that it delivers precise results and reliably establishes a physically meaningful solution. The findings are published in the Journal of the American Chemical Society.

Why molecular electron densities matter

How electrons are distributed in a molecule determines its chemical properties—from its stability and reactivity to its biological effect. Reliably calculating this electron distribution and the resulting energy is one of the central functions of quantum chemistry. These calculations form the basis of many applications in which molecules must be specifically understood and designed, such as for new drugs, better batteries, materials for energy conversion, or more efficient catalysts.

How Artificial Intelligence Is Creating New Job Opportunities

Artificial Intelligence (AI) has become a buzzword in recent years. We’ve heard countless stories about how AI could potentially eliminate jobs, particularly in the engineering and contracting realm. However, we tend to forget that AI is also capable of creating new opportunities for employment and growth. I’d like to explore exactly how AI can help create jobs for engineers and other professionals in the contracting industry.

AI Enhances Demand for Skilled Workers

One of the most significant ways that AI can create jobs is by enhancing efficiency and productivity. By reducing manual labor and streamlining processes, organizations are able to focus their energy on more complex tasks that require human expertise. This shift means a greater need for skilled labor, which means more job openings for engineers and other professionals. For example, AI can be used to automate mundane tasks such as data entry or administrative work, allowing humans to focus their attention on more technical projects – and this means engineers have more time to create solutions that change the world.

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